"""simple docstring"""


import tempfile

import numpy as np
import torch
from transformers import AutoTokenizer, TaEncoderModel

from diffusers import DDPMScheduler, UNetaDConditionModel
from diffusers.models.attention_processor import AttnAddedKVProcessor
from diffusers.pipelines.deepfloyd_if import IFWatermarker
from diffusers.utils.testing_utils import torch_device

from ..test_pipelines_common import to_np






class   __UpperCAmelCase		:

				def 						_a      ( self ):
										torch.manual_seed(0 )
										lowerCamelCase__   =TaEncoderModel.from_pretrained("hf-internal-testing/tiny-random-t5" )

										torch.manual_seed(0 )
										lowerCamelCase__   =AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-t5" )

										torch.manual_seed(0 )
										lowerCamelCase__   =UNetaDConditionModel(
										    sample_size=32      ,					layers_per_block=1      ,					block_out_channels=[32, 64]      ,					down_block_types=[
										        "ResnetDownsampleBlock2D",
										        "SimpleCrossAttnDownBlock2D",
										    ]      ,					mid_block_type="UNetMidBlock2DSimpleCrossAttn"      ,					up_block_types=["SimpleCrossAttnUpBlock2D", "ResnetUpsampleBlock2D"]      ,					in_channels=3      ,					out_channels=6      ,					cross_attention_dim=32      ,					encoder_hid_dim=32      ,					attention_head_dim=8      ,					addition_embed_type="text"      ,					addition_embed_type_num_heads=2      ,					cross_attention_norm="group_norm"      ,					resnet_time_scale_shift="scale_shift"      ,					act_fn="gelu"      ,					)
										unet.set_attn_processor(AttnAddedKVProcessor() )  # For reproducibility tests

										torch.manual_seed(0 )
										lowerCamelCase__   =DDPMScheduler(
										    num_train_timesteps=1000      ,					beta_schedule="squaredcos_cap_v2"      ,					beta_start=0.0_0_0_1      ,					beta_end=0.0_2      ,					thresholding=_lowerCamelCase      ,					dynamic_thresholding_ratio=0.9_5      ,					sample_max_value=1.0      ,					prediction_type="epsilon"      ,					variance_type="learned_range"      ,					)

										torch.manual_seed(0 )
										lowerCamelCase__   =IFWatermarker()

										return {
										    "text_encoder": text_encoder,
										    "tokenizer": tokenizer,
										    "unet": unet,
										    "scheduler": scheduler,
										    "watermarker": watermarker,
										    "safety_checker": None,
										    "feature_extractor": None,
										}

				def 						_a      ( self ):
										torch.manual_seed(0 )
										lowerCamelCase__   =TaEncoderModel.from_pretrained("hf-internal-testing/tiny-random-t5" )

										torch.manual_seed(0 )
										lowerCamelCase__   =AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-t5" )

										torch.manual_seed(0 )
										lowerCamelCase__   =UNetaDConditionModel(
										    sample_size=32      ,					layers_per_block=[1, 2]      ,					block_out_channels=[32, 64]      ,					down_block_types=[
										        "ResnetDownsampleBlock2D",
										        "SimpleCrossAttnDownBlock2D",
										    ]      ,					mid_block_type="UNetMidBlock2DSimpleCrossAttn"      ,					up_block_types=["SimpleCrossAttnUpBlock2D", "ResnetUpsampleBlock2D"]      ,					in_channels=6      ,					out_channels=6      ,					cross_attention_dim=32      ,					encoder_hid_dim=32      ,					attention_head_dim=8      ,					addition_embed_type="text"      ,					addition_embed_type_num_heads=2      ,					cross_attention_norm="group_norm"      ,					resnet_time_scale_shift="scale_shift"      ,					act_fn="gelu"      ,					class_embed_type="timestep"      ,					mid_block_scale_factor=1.4_1_4      ,					time_embedding_act_fn="gelu"      ,					time_embedding_dim=32      ,					)
										unet.set_attn_processor(AttnAddedKVProcessor() )  # For reproducibility tests

										torch.manual_seed(0 )
										lowerCamelCase__   =DDPMScheduler(
										    num_train_timesteps=1000      ,					beta_schedule="squaredcos_cap_v2"      ,					beta_start=0.0_0_0_1      ,					beta_end=0.0_2      ,					thresholding=_lowerCamelCase      ,					dynamic_thresholding_ratio=0.9_5      ,					sample_max_value=1.0      ,					prediction_type="epsilon"      ,					variance_type="learned_range"      ,					)

										torch.manual_seed(0 )
										lowerCamelCase__   =DDPMScheduler(
										    num_train_timesteps=1000      ,					beta_schedule="squaredcos_cap_v2"      ,					beta_start=0.0_0_0_1      ,					beta_end=0.0_2      ,					)

										torch.manual_seed(0 )
										lowerCamelCase__   =IFWatermarker()

										return {
										    "text_encoder": text_encoder,
										    "tokenizer": tokenizer,
										    "unet": unet,
										    "scheduler": scheduler,
										    "image_noising_scheduler": image_noising_scheduler,
										    "watermarker": watermarker,
										    "safety_checker": None,
										    "feature_extractor": None,
										}

				def 						_a      ( self ):
										lowerCamelCase__   =self.get_dummy_components()
										lowerCamelCase__   =self.pipeline_class(**_lowerCamelCase )
										pipe.to(_lowerCamelCase )
										pipe.set_progress_bar_config(disable=_lowerCamelCase )

										lowerCamelCase__   =self.get_dummy_inputs(_lowerCamelCase )

										lowerCamelCase__   =inputs["prompt"]
										lowerCamelCase__   =inputs["generator"]
										lowerCamelCase__   =inputs["num_inference_steps"]
										lowerCamelCase__   =inputs["output_type"]

										if "image" in inputs:
																lowerCamelCase__   =inputs["image"]
										else:
																lowerCamelCase__   =None

										if "mask_image" in inputs:
																lowerCamelCase__   =inputs["mask_image"]
										else:
																lowerCamelCase__   =None

										if "original_image" in inputs:
																lowerCamelCase__   =inputs["original_image"]
										else:
																lowerCamelCase__   =None

										lowerCamelCase__   , lowerCamelCase__   =pipe.encode_prompt(_lowerCamelCase )

										# inputs with prompt converted to embeddings
										lowerCamelCase__   ={
										    "prompt_embeds": prompt_embeds,
										    "negative_prompt_embeds": negative_prompt_embeds,
										    "generator": generator,
										    "num_inference_steps": num_inference_steps,
										    "output_type": output_type,
										}

										if image is not None:
																lowerCamelCase__   =image

										if mask_image is not None:
																lowerCamelCase__   =mask_image

										if original_image is not None:
																lowerCamelCase__   =original_image

										# set all optional components to None
										for optional_component in pipe._optional_components:
																setattr(_lowerCamelCase      ,					_lowerCamelCase      ,					_lowerCamelCase )

										lowerCamelCase__   =pipe(**_lowerCamelCase )[0]

										with tempfile.TemporaryDirectory() as tmpdir:
																pipe.save_pretrained(_lowerCamelCase )
																lowerCamelCase__   =self.pipeline_class.from_pretrained(_lowerCamelCase )
																pipe_loaded.to(_lowerCamelCase )
																pipe_loaded.set_progress_bar_config(disable=_lowerCamelCase )

										pipe_loaded.unet.set_attn_processor(AttnAddedKVProcessor() )  # For reproducibility tests

										for optional_component in pipe._optional_components:
																self.assertTrue(
																    getattr(_lowerCamelCase      ,					_lowerCamelCase ) is None      ,					F'''`{optional_component}` did not stay set to None after loading.'''      ,					)

										lowerCamelCase__   =self.get_dummy_inputs(_lowerCamelCase )

										lowerCamelCase__   =inputs["generator"]
										lowerCamelCase__   =inputs["num_inference_steps"]
										lowerCamelCase__   =inputs["output_type"]

										# inputs with prompt converted to embeddings
										lowerCamelCase__   ={
										    "prompt_embeds": prompt_embeds,
										    "negative_prompt_embeds": negative_prompt_embeds,
										    "generator": generator,
										    "num_inference_steps": num_inference_steps,
										    "output_type": output_type,
										}

										if image is not None:
																lowerCamelCase__   =image

										if mask_image is not None:
																lowerCamelCase__   =mask_image

										if original_image is not None:
																lowerCamelCase__   =original_image

										lowerCamelCase__   =pipe_loaded(**_lowerCamelCase )[0]

										lowerCamelCase__   =np.abs(to_np(_lowerCamelCase ) - to_np(_lowerCamelCase ) ).max()
										self.assertLess(_lowerCamelCase      ,					1E-4 )






				def 						_a      ( self ):
										lowerCamelCase__   =self.get_dummy_components()
										lowerCamelCase__   =self.pipeline_class(**_lowerCamelCase )
										pipe.to(_lowerCamelCase )
										pipe.set_progress_bar_config(disable=_lowerCamelCase )

										lowerCamelCase__   =self.get_dummy_inputs(_lowerCamelCase )
										lowerCamelCase__   =pipe(**_lowerCamelCase )[0]

										with tempfile.TemporaryDirectory() as tmpdir:
																pipe.save_pretrained(_lowerCamelCase )
																lowerCamelCase__   =self.pipeline_class.from_pretrained(_lowerCamelCase )
																pipe_loaded.to(_lowerCamelCase )
																pipe_loaded.set_progress_bar_config(disable=_lowerCamelCase )

										pipe_loaded.unet.set_attn_processor(AttnAddedKVProcessor() )  # For reproducibility tests

										lowerCamelCase__   =self.get_dummy_inputs(_lowerCamelCase )
										lowerCamelCase__   =pipe_loaded(**_lowerCamelCase )[0]

										lowerCamelCase__   =np.abs(to_np(_lowerCamelCase ) - to_np(_lowerCamelCase ) ).max()
										self.assertLess(_lowerCamelCase      ,					1E-4 )



"""simple docstring"""


from __future__ import annotations

from numpy import array, cos, cross, floataa, radians, sin
from numpy.typing import NDArray





def      lowerCamelCase_					(   __lowerCAmelCase  ,       __lowerCAmelCase  ,       __lowerCAmelCase = False							)				->   list[float]:




						'''simple docstring'''



						if radian_mode:
												return [magnitude * cos(__lowerCAmelCase							), magnitude * sin(__lowerCAmelCase							)]
						return [magnitude * cos(radians(__lowerCAmelCase							)							), magnitude * sin(radians(__lowerCAmelCase							)							)]





def      lowerCamelCase_					(   __lowerCAmelCase  ,       __lowerCAmelCase  ,       __lowerCAmelCase = 10**-1							)				->   bool:




						'''simple docstring'''



						lowerCamelCase__   =cross(__lowerCAmelCase  ,       __lowerCAmelCase							)
						lowerCamelCase__   =sum(__lowerCAmelCase							)
						return abs(__lowerCAmelCase							) < eps


if __name__ == "__main__":
	# Test to check if it works
	a				=array(
	    [
	        polar_force(718.4, 180 - 30),
	        polar_force(879.54, 45),
	        polar_force(100, -90),
	    ]
	)

	a				=array([[0, 0], [0, 0], [0, 0]])

	assert in_static_equilibrium(forces, location)

	# Problem 1 in image_data/2D_problems.jpg
	a				=array(
	    [
	        polar_force(30 * 9.81, 15),
	        polar_force(215, 180 - 45),
	        polar_force(264, 90 - 30),
	    ]
	)

	a				=array([[0, 0], [0, 0], [0, 0]])

	assert in_static_equilibrium(forces, location)

	# Problem in image_data/2D_problems_1.jpg
	a				=array([[0, -2000], [0, -1200], [0, 1_5600], [0, -1_2400]])

	a				=array([[0, 0], [6, 0], [10, 0], [12, 0]])

	assert in_static_equilibrium(forces, location)

	import doctest

	doctest.testmod()


